Infectious
diseases are transmitted from person to person, so our understanding
of disease transmission is rooted in a theory of population
transmission dynamics. For Sexually Transmitted Diseases (STDs)
or Blood Borne Infections (BBIs), where transmission requires
an exchange of body fluids, the structure of the contact network
plays a particularly critical role. The contact network can
be represented as a graph, where the persons are nodes, and
the partnerships are edges. Simple mathematical models of disease
transmission dynamics through such networks have provided a
number of insights through simulation that have led to changes
in STD control strategies. Much work has been done in the last
15 years to model HIV transmission, and to collect survey data
on the partnership networks. But the link between data and models
is still problematic. Random graph models, and the techniques
for estimating them, are the natural solution. A class of statistical
exponential family models for random graphs has recently been
adapted from the spatial statistics literature for social networks.
Markov Chain Monte Carlo (MCMC) techniques can be used for likelihood-based
and Bayesian inference. MCMC can also be used to simulate the
network for given parameters, thus linking the network data
to the network simulation. This workshop will cover the recent
advances in network modeling, with applications to disease prevention
and other social science fields. Networks, and their associated
population dynamics, have a broad range of applications in both
the social and physical sciences. The natural audience includes
statisticians, epidemiologists, graph theorists, sociologists,
and those in bio-behavioral health, ecology and evolutionary
biology.